Golden Distance: A New and Comprehensive Metric Definition Study Facilitating Classification Performance Evaluations

Machine/deep learning has great potential in many classification studies that make our lives easier. Performance evaluation of the classification algorithm is of great importance for making accurate predictions in machine learning, which, itself, learns from data without any human intervention. In these learning models, high-dimensional data in classes with complex features belonging to balanced or unbalanced distributions are analysed with different classification algorithms and take their place in the decision stage. While the difficulties in implementing classification algorithms continue today, evaluating the superiority of developed systems over each other continues to be both a time-consuming and very difficult process. In models proposed in the literature, only a single parameter, such as accuracy, is not sufficient to determine model adequacy. In this context, among different classification algorithm performance parameters, some metrics such as sensitivity, specificity, F-measure (F1), Jaccard index, area under the curve, and kappa coefficient are used for system evaluation. As these parameters are placed in the analysis tables, the study should be evaluated at length by including large tables in the article. Instead of using various parameters that make performance analysis difficult in system evaluation, this study defines a new Golden Distance-based parameter that summarizes the model comprehensively from all dimensions. To understand this new metric’s working principle, a detailed comparative analysis of research conducted on three datasets was performed. Based on the final evaluation, the proposed inclusive metric demonstrated successful and fair behaviour in all conditions. Thus, a fair and simple comparison will be achieved with a single metric, and long analyses can be avoided by presenting many performance parameters in systems’ superiority indicator tables.